Tag Archives: mongodb

First off, it’s important to realise that it’s not a snipe at MongoDB – it’s a fine tool.

The lessons are related to mixing multiple databases in a deployment (administration and monitoring overhead) and the acknowledgement that issues of schema design, scalability and maintenance need attention regardless of which brand or technology you pick for your database. That comes back to the old insight that migrations are rarely worth it (regardless of what you migrate to what).

I think these are indeed important considerations as they have a major impact on the ongoing costs of your entire environment (production as well as development and testing) – these days we encounter the “we’re doing this part of our application using MongoDB” approach quite often, so it’s useful to read about and learn from other people’s experience.

With MongoDB there is a particular extra issue to consider, and Dan McKinley also mentions it in his post. NoSQL databases are often also schema-less. However, to keep your data manageable when it grows to significance, you do need to structure it somehow – that is, you need to make sure that (and I’ll just use generic terminology here) in a specific set of records each record contains the required fields. If you don’t, at some point things become unmanageable (or your data ends up as a pile of unusable bits).

Thus, you’re dealing with some form of schema, whether you call it that or not. And you might deal with it in application logic or through some toolkit, rather than in the database itself, but it can’t just be ignored or disregarded. And that’s critical, as often going to a schema-less database is presented as a “then you don’t need to worry about that” change. You do need to “worry” about it: you can pick where the most suitable place is for your needs. If you look at it in that way, you can make an appropriate choice for the particular application at hand.

The No-SQL tag really lumps together a lot of concepts that are in fact as distinct from eachother as they are from SQL/RDBMS.

An object store is not at all similar to Cassandra and Hypertable, which is not at all like an column store. And when looking at BigTable derivatives, it’s quite important to realise that Google actually does joins in middle layers or apps, so while BigTable does not have joins, the apps essentially do use them – I’ve heard it professed that denormalising everything might be a fab idea, but I don’t quite believe in that for all cases, just like I don’t believe in ditching the structured form of RDBMS being the solution.

SQL/RDBMS has had a few decades of dominance now, and has thus become the great “general purpose” tool. With the ascent of all the other tools, it’s definitely worthwhile to look at them, but also realise that each (inluding SQL based ones) have their place. Moving all your stuff wholesale from one to the other is probably a fail.

At the recent OpenSQL Camp in Portland, Brian Aker did a short (7 minute) talk, covering some of these aspects, with a humerous angle. It’s educational, and fun!